This project supports research to improve methods for estimating absolute and attributable risk and collaborative studies to estimate such risks for various cancers. We analyzed data from the Breast Cancer Detection and Demonstration on Project (BCDDP). The intraclass correlation over time was above 0.9, indicating that this strong correlate of breast cancer risk may be useful for predicting risk. The data needed to incorporate mammographic density into models for predicting risk have been assembled, and analytic and computational strategies for incorporating mammographic density have been outlined. Using data from the National Health Inteview Survey, we showed that the proportion of women likely to benefit from using tamoxifen to prevent breast cancer is small, and is confined mainly to young women with high breast cancer risk. We are developing a model to project the individualized absolute risk of colon/rectal cancer. We have constructed relative risk models for proximal and distal colon cancer, based on case-control data, and we have assembled the data required to develop a relative risk model for rectal cancer. We will use population incidence rates to estimte the absolute risk of the earlier of proximal or distal colon cancer or rectal cancer. We gathered data to project the individualized risk of the earlier of ovarian and breast cancer, as well as data for projecting the individualized risk of melanoma. We found that the cumulative incidence to age 48 years among persons with Fanconi's anemia was 10% for leukemia, 11% for bone marrow failure, and 29% for solid tumors. We identified biases that may affect kin-cohort studies when the gene of interest is associated with survival after cancer onset or with the hazards of mortality from competing risks. We developed methods to analyze data on the age at onset of disease from case-control family studies. In these studies, families are ascertained through case or control probands, and survival and covariate data are obtained on relatives. The approach we developed allows estimation of the effects of covariates on the age of onset and on the association of ages at onset among family members without specifying the marginal baseline hazard of time to disease onset, which is estimated non-parametrically. We developed methods of inference on relative risk from case-cohort studies with data missing at random. We developed methods for estimating covariate-adjusted survival curves and population attributable risk from stratified case-cohort studies.